






'''simple docstring'''







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__lowerCAmelCase        =							[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

__lowerCAmelCase        =							[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

__lowerCAmelCase        =							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
  # loop over downblocks/upblocks

  for j in range(2):
    # loop over resnets/attentions for downblocks
    __lowerCAmelCase        =							f"""down_blocks.{i}.resnets.{j}."""
    __lowerCAmelCase        =							f"""input_blocks.{3*i + j + 1}.0."""
    unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

    if i < 3:
      # no attention layers in down_blocks.3
      __lowerCAmelCase        =							f"""down_blocks.{i}.attentions.{j}."""
      __lowerCAmelCase        =							f"""input_blocks.{3*i + j + 1}.1."""
      unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

  for j in range(3):
    # loop over resnets/attentions for upblocks
    __lowerCAmelCase        =							f"""up_blocks.{i}.resnets.{j}."""
    __lowerCAmelCase        =							f"""output_blocks.{3*i + j}.0."""
    unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

    if i > 0:
      # no attention layers in up_blocks.0
      __lowerCAmelCase        =							f"""up_blocks.{i}.attentions.{j}."""
      __lowerCAmelCase        =							f"""output_blocks.{3*i + j}.1."""
      unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

  if i < 3:
    # no downsample in down_blocks.3
    __lowerCAmelCase        =							f"""down_blocks.{i}.downsamplers.0.conv."""
    __lowerCAmelCase        =							f"""input_blocks.{3*(i+1)}.0.op."""
    unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

    # no upsample in up_blocks.3
    __lowerCAmelCase        =							f"""up_blocks.{i}.upsamplers.0."""
    __lowerCAmelCase        =							f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
    unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__lowerCAmelCase        =							"mid_block.attentions.0."
__lowerCAmelCase        =							"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
  __lowerCAmelCase        =							f"""mid_block.resnets.{j}."""
  __lowerCAmelCase        =							f"""middle_block.{2*j}."""
  unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def       __UpperCamelCase					(      lowercase_       :  Dict   ):
       """simple docstring"""




       a_						     =			{k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              a_						     =			sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            a_						     =			v.replace(lowercase_   ,       lowercase_   )
                     a_						     =			v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     a_						     =			v.replace(lowercase_   ,       lowercase_   )
              a_						     =			v
       a_						     =			{v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


# ================#
# VAE Conversion #
# ================#

__lowerCAmelCase        =							[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
  # down_blocks have two resnets
  for j in range(2):
    __lowerCAmelCase        =							f"""encoder.down_blocks.{i}.resnets.{j}."""
    __lowerCAmelCase        =							f"""encoder.down.{i}.block.{j}."""
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

  if i < 3:
    __lowerCAmelCase        =							f"""down_blocks.{i}.downsamplers.0."""
    __lowerCAmelCase        =							f"""down.{i}.downsample."""
    vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

    __lowerCAmelCase        =							f"""up_blocks.{i}.upsamplers.0."""
    __lowerCAmelCase        =							f"""up.{3-i}.upsample."""
    vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

  # up_blocks have three resnets
  # also, up blocks in hf are numbered in reverse from sd
  for j in range(3):
    __lowerCAmelCase        =							f"""decoder.up_blocks.{i}.resnets.{j}."""
    __lowerCAmelCase        =							f"""decoder.up.{3-i}.block.{j}."""
    vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
  __lowerCAmelCase        =							f"""mid_block.resnets.{i}."""
  __lowerCAmelCase        =							f"""mid.block_{i+1}."""
  vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__lowerCAmelCase        =							[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       return w.reshape(*w.shape   ,       1   ,       1   )


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       a_						     =			{k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     a_						     =			v.replace(lowercase_   ,       lowercase_   )
              a_						     =			v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            a_						     =			v.replace(lowercase_   ,       lowercase_   )
                     a_						     =			v
       a_						     =			{v: vae_state_dict[k] for k, v in mapping.items()}
       a_						     =			['q', 'k', 'v', 'proj_out']
       for k, v in new_state_dict.items():
              for weight_name in weights_to_convert:
                     if F'mid.attn_1.{weight_name}.weight' in k:
                            print(F'Reshaping {k} for SD format'   )
                            a_						     =			reshape_weight_for_sd(lowercase_   )
       return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__lowerCAmelCase        =							[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
__lowerCAmelCase        =							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__lowerCAmelCase        =							re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__lowerCAmelCase        =							{"q": 0, "k": 1, "v": 2}


def       __UpperCamelCase					(      lowercase_       :  Tuple   ):
       """simple docstring"""




       a_						     =			{}
       a_						     =			{}
       a_						     =			{}
       for k, v in text_enc_dict.items():
              if (
                  k.endswith('.self_attn.q_proj.weight'   )
                  or k.endswith('.self_attn.k_proj.weight'   )
                  or k.endswith('.self_attn.v_proj.weight'   )
              ):
                     a_						     =			k[: -len('.q_proj.weight'   )]
                     a_						     =			k[-len('q_proj.weight'   )]
                     if k_pre not in capture_qkv_weight:
                            a_						     =			[None, None, None]
                     a_						     =			v
                     continue

              if (
                  k.endswith('.self_attn.q_proj.bias'   )
                  or k.endswith('.self_attn.k_proj.bias'   )
                  or k.endswith('.self_attn.v_proj.bias'   )
              ):
                     a_						     =			k[: -len('.q_proj.bias'   )]
                     a_						     =			k[-len('q_proj.bias'   )]
                     if k_pre not in capture_qkv_bias:
                            a_						     =			[None, None, None]
                     a_						     =			v
                     continue

              a_						     =			textenc_pattern.sub(lambda lowercase_   : protected[re.escape(m.group(0   )   )]   ,       lowercase_   )
              a_						     =			v

       for k_pre, tensors in capture_qkv_weight.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'   )
              a_						     =			textenc_pattern.sub(lambda lowercase_   : protected[re.escape(m.group(0   )   )]   ,       lowercase_   )
              a_						     =			torch.cat(lowercase_   )

       for k_pre, tensors in capture_qkv_bias.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'   )
              a_						     =			textenc_pattern.sub(lambda lowercase_   : protected[re.escape(m.group(0   )   )]   ,       lowercase_   )
              a_						     =			torch.cat(lowercase_   )

       return new_state_dict


def       __UpperCamelCase					(      lowercase_       :  Any   ):
       """simple docstring"""




       return text_enc_dict


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()

  parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
  parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
  parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
  parser.add_argument(
      "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
  )

  __lowerCAmelCase        =							parser.parse_args()

  assert args.model_path is not None, "Must provide a model path!"

  assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

  # Path for safetensors
  __lowerCAmelCase        =							osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
  __lowerCAmelCase        =							osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
  __lowerCAmelCase        =							osp.join(args.model_path, "text_encoder", "model.safetensors")

  # Load models from safetensors if it exists, if it doesn't pytorch
  if osp.exists(unet_path):
    __lowerCAmelCase        =							load_file(unet_path, device="cpu")
  else:
    __lowerCAmelCase        =							osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
    __lowerCAmelCase        =							torch.load(unet_path, map_location="cpu")

  if osp.exists(vae_path):
    __lowerCAmelCase        =							load_file(vae_path, device="cpu")
  else:
    __lowerCAmelCase        =							osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
    __lowerCAmelCase        =							torch.load(vae_path, map_location="cpu")

  if osp.exists(text_enc_path):
    __lowerCAmelCase        =							load_file(text_enc_path, device="cpu")
  else:
    __lowerCAmelCase        =							osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
    __lowerCAmelCase        =							torch.load(text_enc_path, map_location="cpu")

  # Convert the UNet model
  __lowerCAmelCase        =							convert_unet_state_dict(unet_state_dict)
  __lowerCAmelCase        =							{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

  # Convert the VAE model
  __lowerCAmelCase        =							convert_vae_state_dict(vae_state_dict)
  __lowerCAmelCase        =							{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

  # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
  __lowerCAmelCase        =							"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

  if is_vaa_model:
    # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
    __lowerCAmelCase        =							{"transformer." + k: v for k, v in text_enc_dict.items()}
    __lowerCAmelCase        =							convert_text_enc_state_dict_vaa(text_enc_dict)
    __lowerCAmelCase        =							{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
  else:
    __lowerCAmelCase        =							convert_text_enc_state_dict(text_enc_dict)
    __lowerCAmelCase        =							{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

  # Put together new checkpoint
  __lowerCAmelCase        =							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
  if args.half:
    __lowerCAmelCase        =							{k: v.half() for k, v in state_dict.items()}

  if args.use_safetensors:
    save_file(state_dict, args.checkpoint_path)
  else:
    __lowerCAmelCase        =							{"state_dict": state_dict}
    torch.save(state_dict, args.checkpoint_path)












'''simple docstring'''







import math

import qiskit


def       __UpperCamelCase					(      lowercase_       :  int = 1   ,       lowercase_       :  int = 1   ,       lowercase_       :  int = 1   ):
       """simple docstring"""




       if (
           isinstance(lowercase_   ,       lowercase_   )
           or isinstance(lowercase_   ,       lowercase_   )
           or isinstance(lowercase_   ,       lowercase_   )
       ):
              raise TypeError('inputs must be integers.'   )

       if (input_a < 0) or (input_a < 0) or (carry_in < 0):
              raise ValueError('inputs must be positive.'   )

       if (
           (math.floor(lowercase_   ) != input_a)
           or (math.floor(lowercase_   ) != input_a)
           or (math.floor(lowercase_   ) != carry_in)
       ):
              raise ValueError('inputs must be exact integers.'   )

       if (input_a > 2) or (input_a > 2) or (carry_in > 2):
              raise ValueError('inputs must be less or equal to 2.'   )

       # build registers
       a_						     =			qiskit.QuantumRegister(4   ,       'qr'   )
       a_						     =			qiskit.ClassicalRegister(2   ,       'cr'   )
       # list the entries
       a_						     =			[input_a, input_a, carry_in]

       a_						     =			qiskit.QuantumCircuit(lowercase_   ,       lowercase_   )

       for i in range(0   ,       3   ):
              if entry[i] == 2:
                     quantum_circuit.h(lowercase_   )  # for hadamard entries
              elif entry[i] == 1:
                     quantum_circuit.x(lowercase_   )  # for 1 entries
              elif entry[i] == 0:
                     quantum_circuit.i(lowercase_   )  # for 0 entries

    # build the circuit
       quantum_circuit.ccx(0   ,       1   ,       3   )  # ccx = toffoli gate
       quantum_circuit.cx(0   ,       1   )
       quantum_circuit.ccx(1   ,       2   ,       3   )
       quantum_circuit.cx(1   ,       2   )
       quantum_circuit.cx(0   ,       1   )

       quantum_circuit.measure([2, 3]   ,       lowercase_   )  # measure the last two qbits

       a_						     =			qiskit.Aer.get_backend('aer_simulator'   )
       a_						     =			qiskit.execute(lowercase_   ,       lowercase_   ,       shots=1_000   )

       return job.result().get_counts(lowercase_   )


if __name__ == "__main__":
  print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")




